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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

EVI-Equity

EVI-Equity (Electric Vehicle Infrastructure for Equity) is a $200k project, started around in June of 2021, with a funding from the Vehicle Technologies Office (VTO). The motivation was to create a new analytical capability that can enable us to quantify and investigate equitable access to and distribution of existing and future deployment of PEVs and EVSEs in neighborhoods, cities, states, and the nation. EVI-Equity is a bottom-up equity-focused analysis model, built upon individual (synthetic) households, aggregated by census block groups. It consists of four core components - community engagement, environmental profiling, household expenditures, and network design. Although there are some commonalities, EVI-Equity is not a vehicle choice model, charging simulation model, or transportation demand model. EVI-Equity is rather a cross-cutting analysis tool, dedicated for evaluating equitable EV adoption and EVSE deployment, encompassing and bridging a wide variety of related tools, models, and frameworks. Some of the results indicate the importance of used vehicle market for low-income households. The presentation also highlights similarities and variations as to preferred public charging locations. For example, regardless of household income, retail spots are the most preferred location for public charging, followed by curbside/street. However, the results also imply that the importance of workplace charging may vary with income - the lower the income, the less important. Environmental profiling results, with an example of ground-level ozone in Atlanta area, show that the contrast between the haves and the have nots of plug-in electric vehicles depends on location. The assessment of household expenditures illustrates the economic impact of home charging access on an individual household level - the lower the income, the greater the impact is. Lastly, Denver metro area and the state of South Dakota are used to showcase the impact of different network design of charging infrastructure, as well as alternative (vs. baseline/existing) electric vehicle adoption pattern.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2023

Spatiotemporal Studies of Soluble Inorganic Nanostructures with X‐rays and Neutrons

Abstract This Review addresses the use of X‐ray and neutron scattering as well as X‐ray absorption to describe how inorganic nanostructured materials assemble, evolve, and function in solution. We first provide an overview of techniques and instrumentation (both large user facilities and benchtop). We review recent studies of soluble inorganic nanostructure assembly, covering the disciplines of materials synthesis, processes in nature, nuclear materials, and the widely applicable fundamental processes of hydrophobic interactions and ion pairing. Reviewed studies cover size regimes and length scales ranging from sub‐Ångström (coordination chemistry and ion pairing) to several nanometers (molecular clusters, i.e. polyoxometalates, polyoxocations, and metal‐organic polyhedra), to the mesoscale (supramolecular assembly processes). Reviewed studies predominantly exploit 1) SAXS/WAXS/SANS (small‐ and wide‐angle X‐ray or neutron scattering), 2) PDF (pair‐distribution function analysis of X‐ray total scattering), and 3) XANES and EXAFS (X‐ray absorption near‐edge structure and extended X‐ray absorption fine structure, respectively). While the scattering techniques provide structural information, X‐ray absorption yields the oxidation state in addition to the local coordination. Our goal for this Review is to provide information and inspiration for the inorganic/materials science communities that may benefit from elucidating the role of solution speciation in natural and synthetic processes.

Yin, Jia‐Fu↗
DOE OSTI2023

Spatiotemporal Studies of Soluble Inorganic Nanostructures with X‐rays and Neutrons

This Review addresses the use of X-ray and neutron scattering as well as X-ray absorption to describe how inorganic nanostructured materials assemble, evolve, and function in solution. We first provide an overview of techniques and instrumentation (both large user facilities and benchtop). We review recent studies of soluble inorganic nanostructure assembly, covering the disciplines of materials synthesis, processes in nature, nuclear materials, and the widely applicable fundamental processes of hydrophobic interactions and ion pairing. Reviewed studies cover size regimes and length scales ranging from sub-Ångström (coordination chemistry and ion pairing) to several nanometers (molecular clusters, i.e. polyoxometalates, polyoxocations, and metal-organic polyhedra), to the mesoscale (supramolecular assembly processes). Reviewed studies predominantly exploit 1) SAXS/WAXS/SANS (small- and wide-angle X-ray or neutron scattering), 2) PDF (pair-distribution function analysis of X-ray total scattering), and 3) XANES and EXAFS (X-ray absorption near-edge structure and extended X-ray absorption fine structure, respectively). While the scattering techniques provide structural information, X-ray absorption yields the oxidation state in addition to the local coordination. Our goal for this Review is to provide information and inspiration for the inorganic/materials science communities that may benefit from elucidating the role of solution speciation in natural and synthetic processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2021

Northern Great Plains Disasters: Using Earth Observations to Enhance Flood Monitoring on Tribal Lands in the Northern Great Plains

In 2019, the Great Plains experienced unprecedented catastrophic flooding. Large flood events are predicted to increase in frequency and severity, posing risks to communities in this region, particularly Tribal Nations. We used data from Sentinel-1 C-band Synthetic Aperture Radar (C-SAR), imagery from the Sentinel-2 MultiSpectral Instrument (MSI), and digital elevation models (DEMs) from the Shuttle Radar Topography Mission (SRTM) within Google Earth Engine to map historical floods in the region beginning in 2014 with particular attention to the Rosebud Sioux Reservation and the tribal lands of other Great Plains Tribal Water Alliance members. This historical mapping used C-SAR for a combined method approach with a Z-score algorithm in addition to an index for flooded short vegetation. We also developed a flood risk map by weighting different flood predictor variables according to flood risk literature. These variables included soil drainage from the Soil Survey Geographic Database (SSURGO); elevation, slope, and Topographic Wetness Index (TWI) derived from digital elevation models; precipitation from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS); land cover from the National Land Cover Database (NLDC); and Normalized Difference Vegetation Index (NDVI) derived from Landsat 8 Operational Land Imager (OLI). From the flood extent and risk maps, we identified widespread flooding in short vegetation (including cropland) and noted flood susceptibility in regions exhibiting high social vulnerability and low community resilience (FEMA indices). We created an ArcGIS Online StoryMap to share project background, results, and data. Additionally, we provided a written tutorial so partners may replicate the flood mapping for future flood events.

Anna Ballasiotes↗
DOE OSTI2023

Methodology for physics-informed generation of synthetic neutron time-of-flight measurement data

Accurate neutron cross section data are a vital input to the simulation of nuclear systems for a wide range of applications from energy production to national security. The evaluation of experimental data is a key step in producing accurate cross sections. There is a widely recognized lack of reproducibility in the evaluation process due to its artisanal nature and therefore there is a call for improvement within the nuclear data community. This can be realized by automating/standardizing viable parts of the process, namely, parameter estimation by fitting theoretical models to experimental data. This automation effort could greatly benefit from a synthetic data resource. This work leverages problem-specific physics, Monte Carlo sampling, and a general methodology for data synthesis to generate unlimited, labelled experimental cross-section data that is statistically indistinguishable to the observed data. Heuristic and, where applicable, rigorous statistical comparisons to observed data support this claim. The demonstration is based on/limited to transmission measurements at Rensselaer Polytechnic Institute (RPI) and energy-differential cross sections in the resolved resonance region (RRR). An open-source software is published alongside this article that executes the complete methodology to produce high-utility synthetic datasets. The goal of this work is to provide an approach and corresponding tool that will allow the evaluation community to begin exploring more data-driven, ML-based solutions to long-standing challenges in the field.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS2019

2018 NISAR Applications Workshop: Agriculture and Soil Moisture

Agricultural lands cover the globe and play an essential role in not only sustaining a growing global population, but can have significant implications on the Earth system through land use change (e.g., deforestation, grazing, etc.). As such, countries around the world have dedicated programs for managing these lands. Accurate and timely information concerning the status of agricultural crops (soil moisture, crop health, crop type, etc.) is essential to those nations’ anthropogenic and ecological health as well as economy. The joint NASA/US Department of Agriculture Agricultural Research Service (USDA-ARS) workshop focused on advancing agriculture and soil moisture applications by using remote sensing data from the NASA-ISRO Synthetic Aperture Radar (NISAR) mission (expected launch 2022). Participants included representatives from the international agriculture community that are key players in facilitating integration of Earth Observations into decision support workflows including US Federal Agencies, nonprofits, and private sector. They included scientists, technicians, and program managers with a responsibility for data acquisition and exploitation such as product development, delivery, and use, as well as capacity building. Discussions were held over two and a half days to convey the broader agriculture and soil moisture community information needs, the mission and procedures for various representative participants and programs involved in the delivery of geospatial products, and the capabilities and status of the NISAR mission. Case studies were presented to demonstrate the current state of practice in the use of SAR remote sensing for applications of direct importance for the agriculture and soil moisture communities. Eleven organizations presented their information requirements in response to a set of questions provided by the NASA team, then the NASA team responded by describing the degree to which NISAR could meet these requirements. Discussion ensued about needed data product specifications to increase utility (e.g., projection, latency, etc.), tools and capacity building.

Stavros, Natasha↗
DOE OSTI2024

Situational awareness-enhancing community-level load mapping with opportunistic machine learning

Motivated by present and forthcoming challenges in the adoption and integration of distributed renewable energy, we develop a machine learning (ML) approach that builds short-fuse mappings connecting the occasionally-unobservable true load in one target community with information-rich signals collected from relatively more instrumented reference communities. Our setting is inspired by and tailored to target communities with significant unobservable behind-the-meter solar generation, where true load (a relatively well-behaved quantity of interest to grid operators) is hard to discern during daytime due to insufficient instrumentation and/or privacy reasons, but that can be related to reference communities with low unobservable distributed variable generation or with sufficient instrumentation. The developed mapping, herein realized with Support Vector Machine regression, is built using nighttime data from all communities, when their distributed generation is low or zero. Our ML algorithm opportunistically learns to correlate signals of interest and then is operationally used the next day to shed light into target community load evolution. The mapping is subsequently rebuilt, rolling its short-fuse scope perpetually forward in time. Here, we demonstrate the efficacy of our approach on nine synthetically generated topologies and associated timeseries stemming from real-world data, on which we observe cumulative error performance that yields lower than 10% and 15% daily-averaged mean absolute percentage errors in target community load estimation on more than about 75% and 90% of days, respectively, in multiple yearly evaluations that shed light on long-term performance also under seasonal and one-off effects. The proposed ML-powered methodology can offer grid operators much-improved visibility into a previously obscure space and can also serve as an additional source of information in broader, multi-modal solar disaggregation solutions.

14 SOLAR ENERGY↗
DOE OSTI2022

A differentiable approach to the maximum independent set problem using dataless neural networks

The success of machine learning solutions for reasoning about discrete structures has brought attention to its adoption within combinatorial optimization algorithms. Such approaches generally rely on supervised learning by leveraging datasets of the combinatorial structures of interest drawn from some distribution of problem instances. Reinforcement learning has also been employed to find such structures. Here, in this paper, we propose a different approach in that no data is required for training the neural networks that produce the solution. In this sense, what we present is not a machine learning solution, but rather one that is dependent on neural networks and where backpropagation is applied to a loss function defined by the structure of the neural network architecture as opposed to a training dataset. In particular, we reduce the popular combinatorial optimization problem of finding a maximum independent set to a neural network and employ a dataless training scheme to refine the parameters of the network such that those parameters yield the structure of interest. Additionally, we propose a universal graph reduction procedure to handle large-scale graphs. The reduction exploits community detection for graph partitioning and is applicable to any graph type and/or density. Experimental results on both real and synthetic graphs demonstrate that our proposed method performs on par or outperforms state-of-the-art learning-based methods in terms of the size of the found set without requiring any training data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Data-Informed Synthetic Networks of Water Distribution Systems for Resilience Analysis in Puerto Rico

The increasing potential of infrastructure disruptions calls for high-quality infrastructure models to be used in resilience analysis and decision making. Unfortunately, many utilities and communities do not have access to accurate and detailed models due to a lack of data and resources. Furthermore, security restrictions on sharing infrastructure models present roadblocks to research, analysis, and decision making. Recent advances in the development of synthetic water distribution models provide a potential solution to this problem. There is an opportunity to improve these methods by leveraging incomplete pipe datasets to aid synthetic network generation. To address this gap, we developed a methodology for synthetic network generation that incorporates partial pipe data using a modification of the minimum cost flow algorithm for network generation and pipe sizing. This methodology demonstrates how partial pipe data can be leveraged to improve site-specific synthetic network generation. For the study area of Mayagüez, Puerto Rico, a synthetic model generated using 50% of real pipe data matches the pressure of the validation system with an average error of 23.5 m of head, which improves upon the average error of 31.6 m of head produced by a synthetic model generated using no data of the real pipes. Additionally, synthetic networks are shown to replicate the pressure response under a disruption scenario of the validation network, suggesting potential use in resilience analysis.

resilience analysis↗
DOE OSTI2021

Plant Synthetic Biology Conference – Travel Awardees and Speakers

The overarching goals of the Plant Synthetic Biology (SynBio) Conference, convened by the American Society of Plant Biologists (ASPB), were to raise community consciousness of what SynBio is and what it can do in key areas, including crop productivity, specialty chemicals, biosensors, and microbe-plant interactions. Within that context, the conference had two broad objectives. First, to feature a set of leading university and industry speakers from the US and Europe whose research on plants and microbes spans the SynBio spectrum with respect both to technology and to applications, as well as short talks (selected from submitted abstracts) on cutting-edge topics. And second, to foster exchanges among public sector scientists and industry scientists, whose participation as poster presenters, exhibitors, and sponsors was actively encouraged because so much innovative SynBio work is being carried out at dynamic young companies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

EpiCast: Simulating Epidemics with Extreme Detail

In early 2020, COVID-19 swept the globe. Governments attempted to “flatten the curve” through business shutdowns and stay-at-home orders, but the United States was hit hard. By the end of March, mere months after the virus first emerged in humans 7,000 miles away, the U.S. had recorded 192,300 cases and 5,300 deaths. While this unprecedented disaster sent shockwaves through every level of society and clouded an uncertain future, state and local governments turned to computational and mathematical epidemiology researchers to help formulate intervention strategies to limit the spread of the disease. Traditional forecasting models provided a reasonable understanding of how the near future was likely to look, but local policy makers and public health communities still struggled to understand how potential mitigations ought to be implemented. Decision makers needed a way to measure the impact of their policy choices—they needed better technology. EpiCast answered the call, bringing urgently needed answers to policymakers grappling with how to adjust school and business schedules. EpiCast is modeling software that generates a synthetic, representative population to simulate infectious disease transmission in the United States with extreme detail and granularity. The software models human behavior combined with community-specific information to provide a fine-grained preview of the effect of potential mitigation strategies for decision makers.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Modeling, Load Profile Validation, and Assessment of Solar-Rooftop Energy Potential for Low-and-Moderate-Income Communities in the Caribbean

This document presents the modeling of load profile consumption for Low-and-Moderate-Income (LMI) communities in the Caribbean Islands, as well as an assessment of the solar-rooftop energy potential. In this work, real data, together with synthetic and electricity bill data, were collected to validate and improve the load profile models. The solar-rooftop energy potential was obtained through a National Renewable Energy Laboratory (NREL) software called the PVWatts calculator, and mathematical analysis. The analysis of rooftop solar energy potential was conducted to enable the minimum size of solar power systems to fit the energy demand in the community. The results obtained allow estimation of the capacity of the energy system for each house or an entire community.

14 SOLAR ENERGY↗
NASA NTRS2013

Height Error Correction for the New SRTM Elevation Product

The Shuttle Radar Topography Mission (SRTM), carrying a single-pass interferometric synthetic aperture radar(SAR) instrument, collected a global elevation data set, which has been widely used in scientific, military and commercial communities. In the new proposed NASA SRTM reprocessing task, the SRTM elevation data is going to be processed at higher spatial resolution and with improved height accuracy. Upon completion, the improved SRTM product will be freely available. This paper describes the calibration approaches for reduction of elevation ripple effects and height accuracy improvements.

Synthetic Aperture Radar Interferometry (InSAR)↗
NASA NTRS1995

User guide to the Magellan synthetic aperture radar images

The Magellan radar-mapping mission collected a large amount of science and engineering data. Now available to the general scientific community, this data set can be overwhelming to someone who is unfamiliar with the mission. This user guide outlines the mission operations and data set so that someone working with the data can understand the mapping and data-processing techniques used in the mission. Radar-mapping parameters as well as data acquisition issues are discussed. In addition, this user guide provides information on how the data set is organized and where specific elements of the set can be located.

Wall, Stephen D.↗
DOE OSTI2022

5th International Conference on Plant Synthetic Biology, Bioengineering, and Biotechnology (Final Technical Report)

The 5th International Conference on Plant Synthetic Biology, Bioengineering, and Biotechnology will be held virtually from November 15 to 17, 2021. The meeting is organized by the American Institute of Chemical Engineers (AIChE), which has an excellent and long-standing record convening scientific conferences and events. This conference has been held annually since 2016 and it is expected to as successful as previous conferences. The planned sessions featured BER-relevant topics including mew technologies, bioenergy, bioproducts and materials, plant metabolic engineering, crops for future climates, and biosafety. The event brought together scientists and engineers from universities, industry, and government working in all aspects of plant synthetic biology, plant bioengineering and plant biotechnology. The conference provided a forum for leading scientists to interact with new investigators and students and engage in discussions on the latest scientific advances and technology developments in plant synthetic biology and genome engineering. The organizing committee and meeting participants include diverse members of the BER research community.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

UrbanPop 2019 Baseline Population

A synthetic residential baseline population for the United States at the census block group level based on the American Community Survey 2015-2019 5-Year Estimates and described by residential, demographic, social, economic, student, mobility, and housing characteristics.

Tuccillo, Joe [ORNL] (ORCID:0000000259300943)↗
NASA NTRS1993

Benefits associated with advanced technologies applied to a high-speed civil transport concept

Results of a first-order assessment of the mission performance benefits associated with the technology improvements and goals of the Phase II High-Speed Research (HSR) Program are presented. A breakdown of the four major disciplines resulted in the following estimated TOGW savings from the 1990 vehicle: propulsion at 14.3 percent, structures at 11.7 percent, flight-deck systems at 4.0 percent, and aerodynamics at 15.0 percent. Based on 100 percent success of the HSR Phase II proposed technology advancements, the overall combined impact is estimated to result in a 45 percent reduction in TOGW from a 1990 entry-into-service (EIS) date, which could result in a viable 2005 EIS vehicle with an acceptable TOGW that meets Stage III community noise restrictions. Through supersonic laminar flow control and the possible reduction in reserve fuel requirements resulting from synthetic vision capability, the potential exists for an additional 9.6 percent reduction in TOGW.

Ozoroski, L. P.↗
DOE OSTI2020

Zr 6 O 8 Node-Catalyzed Butene Hydrogenation and Isomerization in the Metal–Organic Framework NU-1000

Zirconium-based metal–organic frameworks (Zr-MOFs) have been increasingly studied over the past two decades as heterogeneous catalysts due to their synthetic tunability, well-defined nature, and chemical stability. In contrast to traditional zirconia-based heterogeneous catalysts, the community has assumed that Zr-MOFs are inert catalyst supports that do not participate directly in hydrocarbon transformations, such as olefin hydrogenation and isomerization. Here, we report that the Zr-MOF NU-1000 is capable of catalyzing olefin hydrogenation and isomerization, without any postsynthetic modifications, under a hydrogen atmosphere. We probe H 2 activation over the nodes of NU-1000 via spectroscopic and computational techniques revealing that H 2 dissociation can occur heterolytically across coordinatively unsaturated Zr sites and proximal hydroxide and μ3-oxo ligands. These results, along with catalytic experiments, suggest that H 2 activation results in node-supported zirconium hydrides capable of the hydrogenation and isomerization of 1-butene. When examining rate dependence on the partial pressure of H 2 , we observe first-order dependence for hydrogenation and half-order dependence for isomerization. Half-order H 2 rate dependence is consistent with a mechanism where both fragments of cleaved H 2 are active for 1-butene isomerization, suggesting that heterolytic cleavage generates acidic protons resulting in parallel, acid-, and hydride-catalyzed isomerization pathways. In conclusion, this work shows that Zr-MOFs have more diverse reactivity than the current literature may suggest and opens possibilities for ways in which Zr-MOFs can be used as heterogeneous catalysts and supports.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗